This study contributes to the literature on the export-led growth (ELG) hypothesis by adopting a semiparametric approach under two levels of temporal aggregation to investigate the ELG hypothesis in the Philippines. To assess the impact of model specification on the ELG hypothesis, parametric and semiparametric ECMs are estimated using Philippine annual and quarterly data on GDP, exports, exchange rates and gross fixed-capital formation, focusing on the role of exchange rates.

The causal relationship between exports and economic growth is examined using the Granger-causality procedure. It can be concluded that for the Philippines, the ELG hypothesis is (a) sensitive to model specification, (b) affected by different levels of temporal aggregation, and (c) by the inclusion or exclusion of exchange rates.

Under short-run and total causality tests, parametric and semiparametric analyses using annual data support export-led growth and bidirectional causality, respectively, and no causal relation between exports and output in the long run. Quarterly data analysis revealed that, in the long run, parametric and semiparametric procedures support bidirectional causality and growth-led exports, respectively, and that there is bidirectional causality between exports and economic growth for short-run and total causality tests.

Using annual data, total causality tests support export-led growth and no causality, with the inclusion and exclusion of exchange rates, respectively. No change in results is evident for short-run and long-run causality tests. Using quarterly data, no change in results is shown in all Granger causality tests.

The general results on bidirectional causality between exports and economic growth suggest that the Philippines could enjoy economic prosperity by strengthening their trade and investment policy and gearing it towards opening up the economy.

Previous studies have argued that differences in outcomes of the ELG hypothesis tests may be due to different levels of temporal aggregation, methodologies, model misspecification, and omitted variables. This analysis introduces empirical evidence on these issues.